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首页> 外文会议>10th IEEE International Conference on Data Mining Workshops >Using Self-Organizing Map and Heuristics to Identify Small Statistical Areas Based on Household Socio-Economic Indicators in Turkey's Address Based Population Register System
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Using Self-Organizing Map and Heuristics to Identify Small Statistical Areas Based on Household Socio-Economic Indicators in Turkey's Address Based Population Register System

机译：在土耳其基于地址的人口登记系统中，使用自组织地图和启发式方法基于家庭社会经济指标识别较小的统计区域

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摘要

Census operations are very important events in the history of a nation. These operations cover every bit of land and property of the country and its citizens. The publication of census based on spatial units is one of the important problems of national statistical organizations, which requires determination of small statistical areas (SSAs) or so called census geography. Since 2006, Turkey aims to produce census data not as ȁC;de-factoȁD; (static) but as ȁC;de-jureȁD; (real-time) by the new Address Based Register Information System (ABPRS). Besides, by this new register based census, personal information is matched with their address information and censuses gained a spatial dimension. However, as Turkey lacks SSAȁ9;s, the data cannot be published in smaller spatial granularities. In this study, it is aimed to employ a spatial clustering and districting methodology to automatically produce SSAs which are basically built upon the ABPRS data that is geo-referenced with the aid of geographical information systems (GIS). For its realization, simulated annealing on k-means clustering of Self-Organizing Map (SOM) unified distances is employed to produce SSAȁ9;s for ABPRS. This method is basically implemented on block datasets having either raw census data or socio-economic status (SES) indices obtained from census data. The resulting SSAȁ9;s are evaluated for the case study area.
机译：人口普查是一个国家历史上非常重要的事件。这些行动覆盖了该国及其公民的每片土地和财产。基于空间单位的人口普查的发布是国家统计组织的重要问题之一，这需要确定小的统计区域（SSA）或所谓的人口普查地理。自2006年以来，土耳其的目标是生成人口普查数据，而不是ȁC;事实ȁD; （静态），但以ȁC;de-jureȁD; （新）基于地址的注册信息系统（ABPRS）。此外，通过这种基于新登记的人口普查，个人信息与他们的地址信息相匹配，人口普查获得了空间维度。但是，由于土耳其缺乏SSAȁ9，因此无法以较小的空间粒度发布数据。在本研究中，旨在采用空间聚类和分区方法自动生成SSA，这些SSA基本基于借助地理信息系统（GIS）进行地理参考的ABPRS数据。为了实现这一目标，对自组织图（SOM）统一距离的k均值聚类进行了模拟退火，以生成用于ABPRS的SSAȁ9; s。该方法基本上是在具有原始普查数据或从普查数据获得的社会经济地位（SES）指数的块数据集上实现的。对所得的SSAȁ9; s进行案例研究区域的评估。

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